{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [GaussianNB 高斯](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB)\n",
    "- [MultinomialNB 多项式](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB # 高斯和多项式朴素贝叶斯\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 设置属性防止中文乱码\n",
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 花萼长度、花萼宽度，花瓣长度，花瓣宽度\n",
    "iris_feature_E = 'sepal length', 'sepal width', 'petal length', 'petal width'\n",
    "iris_feature_C = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'\n",
    "iris_class = 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'\n",
    "features = [2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总样本数目：150；特征属性数目：2\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "## 读取数据\n",
    "path = './datas/iris.data'  # 数据文件路径\n",
    "data = pd.read_csv(path, header=None)\n",
    "x = data[list(range(4))]\n",
    "x = x[features]\n",
    "y = pd.Categorical(data[4]).codes ## 直接将数据特征转换为0，1,2\n",
    "print (\"总样本数目：%d；特征属性数目：%d\" % x.shape)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据集样本数目：120, 测试数据集样本数目：30\n"
     ]
    }
   ],
   "source": [
    "## 0. 数据分割，形成模型训练数据和测试数据\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x, y, train_size=0.8, random_state=14)\n",
    "x_train, x_test, y_train, y_test = x_train1, x_test1, y_train1, y_test1\n",
    "print (\"训练数据集样本数目：%d, 测试数据集样本数目：%d\" % (x_train.shape[0], x_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('sc', StandardScaler(copy=True, with_mean=True, with_std=True)), ('poly', PolynomialFeatures(degree=1, include_bias=True, interaction_only=False)), ('clf', GaussianNB(priors=None))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 高斯贝叶斯模型构建\n",
    "clf = Pipeline([\n",
    "        ('sc', StandardScaler()), # 标准化，转换为高斯分布，这里用归一化效果不好\n",
    "        ('poly', PolynomialFeatures(degree=1)),\n",
    "        ('clf', GaussianNB())])\n",
    "## 训练模型\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集准确度: 95.83%\n",
      "测试集准确度：96.67%\n"
     ]
    }
   ],
   "source": [
    "## 计算预测值并计算准确率\n",
    "y_train_hat = clf.predict(x_train)\n",
    "print ('训练集准确度: %.2f%%' % (100 * accuracy_score(y_train, y_train_hat)))\n",
    "y_test_hat = clf.predict(x_test)\n",
    "print ('测试集准确度：%.2f%%' % (100 * accuracy_score(y_test, y_test_hat)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 产生区域图\n",
    "N, M = 500, 500     # 横纵各采样多少个值\n",
    "x1_min1, x2_min1 = x_train.min()\n",
    "x1_max1, x2_max1 = x_train.max()\n",
    "x1_min2, x2_min2 = x_test.min()\n",
    "x1_max2, x2_max2 = x_test.max()\n",
    "x1_min = np.min((x1_min1, x1_min2))\n",
    "x1_max = np.max((x1_max1, x1_max2))\n",
    "x2_min = np.min((x2_min1, x2_min2))\n",
    "x2_max = np.max((x2_max1, x2_max2))\n",
    "\n",
    "t1 = np.linspace(x1_min, x1_max, N)\n",
    "t2 = np.linspace(x2_min, x2_max, N)\n",
    "x1, x2 = np.meshgrid(t1, t2)  # 生成网格采样点\n",
    "x_show = np.dstack((x1.flat, x2.flat))[0] # 测试点\n",
    "\n",
    "cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])\n",
    "cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])\n",
    "y_show_hat = clf.predict(x_show)                  # 预测值\n",
    "y_show_hat = y_show_hat.reshape(x1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xZGNjQxzHlShonjp1iiQSiZp5Sxc//fTTGzO/8cepU6fU6njy5AnZ29uTvb29YGbNz88n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\nAR2/h0Km8RVNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0adf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 画图\n",
    "plt.figure(facecolor='w')\n",
    "plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light)     # 预测值的显示\n",
    "plt.scatter(x_train[features[0]], x_train[features[1]], c=y_train, edgecolors='k', s=50, cmap=cm_dark)\n",
    "plt.scatter(x_test[features[0]], x_test[features[1]], c=y_test, marker='^', edgecolors='k', s=120, cmap=cm_dark)\n",
    "plt.xlabel(iris_feature_C[features[0]], fontsize=13)\n",
    "plt.ylabel(iris_feature_C[features[1]], fontsize=13)\n",
    "plt.xlim(x1_min, x1_max)\n",
    "plt.ylim(x2_min, x2_max)\n",
    "plt.title(u'GaussianNB对鸢尾花数据的分类结果, 正确率:%.3f%%' % (100 * accuracy_score(y_test, y_test_hat)), fontsize=18)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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